---
title: "Final-ish Project Graphs"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: scroll
  html_document:
    df_print: paged
---
# Corona Dashboard
```{r import libraries}
library(tidyverse)
library(lubridate) # dates
library(chron) # reverse date plot
library(formattable) # round numbers
library(ggplot2)
library(scales)
library(RcppRoll) # rolling average
library(flexdashboard) # html dashboard formatting
```




```{r set colours}
maroon <- "#60223b"
mustard <- "#F1B434"
darkgrey <- "#333333"
lightgrey <- "#8c979a"
black <- "#000000"
engineering_yellow <- "#eaaa00"
arts_orange <- "#FF8F1C"
military_salmon <- "#e56a54"
science_red <- "#cb333b"
law_winered <- "#9e2629"
theology_purple <- "#84329B"
education_darkblue <- "#326295"
ems_lightblue <- "#2dccd3"
agri_green <- "#509e2f"
med_bluegreen <- "#006163"
```


```{r read data}
# general (cumulative) information regarding tests, deaths, and recoveries. Complete timeline.
tests_deaths_recoveries_data <- read.csv("data/covid19za_timeline_testing.csv")

# total (cumulative) number of confirmed cases by province
provincial_total_confirmed <- read.csv("data/covid19za_provincial_cumulative_timeline_confirmed.csv")

# provincial deaths, and recoveries
provincial_recoveries_data<- read.csv("data/covid19za_provincial_cumulative_timeline_recoveries.csv")

# lockdown levels
national_lockdown<- read.csv("data/national_lockdown_govza.csv")

# death data
death_data <- read.csv("data/covid19za_provincial_cumulative_timeline_deaths.csv")

# owid SA extracted (big) data
owid_sa_data <- read.csv("data/owid-covid-data_SouthAfricaExtracted_big.csv")
```


```{r value box death and recovered}
# total deaths
total_deaths <- max(tests_deaths_recoveries_data$deaths, na.rm = TRUE)

# get last day's number of deaths increase
last_death_cols <- tail(tests_deaths_recoveries_data$deaths, n=2)
death_day_update <- last_death_cols[2] - last_death_cols[1]

# total recovered
total_recovered <- max(tests_deaths_recoveries_data$recovered, na.rm = TRUE)

# percentage recovered
percentage_recovered <- total_recovered / (total_recovered + total_deaths) * 100
```

```{r value box active and confirmed cases}
# total confirmed cases
total_confirmed <- max(provincial_total_confirmed$total, na.rm = TRUE)

# get last day's number of confirmed increase
last_confirmed_cols <- tail(provincial_total_confirmed$total, n=2)
confirmed_day_update <- last_confirmed_cols[2] - last_confirmed_cols[1]

# active cases = confirmed cases  - recoveries - deaths
total_active <- total_confirmed - total_recovered - total_deaths
```

```{r value box tests}
# get number of tests
total_tests <- max(tests_deaths_recoveries_data$cumulative_tests, na.rm = TRUE)
# get last day's number of tests increase
last_tests_cols <- tail(tests_deaths_recoveries_data$cumulative_tests, n=2)
test_day_update <- last_tests_cols[2] - last_tests_cols[1]
```

```{r bubble new tests and cases}
bubble_data <- owid_sa_data %>% 
  select(date, new_cases, new_tests) %>% 
  filter(!is.na(new_cases)) %>% 
  filter(!is.na(new_tests)) %>% 
  mutate(date = as.Date(date))
```

```{r last 20 days new cases}
# Get total confirmed cases per date
confirmed <- owid_sa_data %>% 
  filter(!is.na(new_cases)) %>% 
  select(date, new_cases) %>% 
  mutate(date = as.Date(date)) %>% 
  filter(date > as.Date("2021-06-01"))
confirmed <- confirmed %>% map_df(rev) 
```


Row {data-width=400}
-----------------------------------------------------------------------

### confirmed cases {.value-box}

```{r plot confirmed cases box}
valueBox(
  value = paste(format(total_confirmed, big.mark = ","), " (+ ", confirmed_day_update,")", sep = ""),
  caption = "CONFIRMED CASES",
  icon = "fas fa-user-md",
  color = maroon
)
```



### total deaths {.value-box}

```{r plot total deaths box}
valueBox(
  value = paste(format(total_deaths, big.mark = ","), " (+ ", death_day_update,")", sep = ""),
  caption = "DEATHS",
  icon = "fas fa-user-md",
  color = science_red
)
```



### total tests {.value-box}

```{r plot total tests box}
valueBox(
  value = paste(format(total_tests, big.mark = ","), " (+ ", test_day_update,")", sep = ""),
  caption = "TESTS",
  icon = "fas fa-user-md",
  color = med_bluegreen
)
```

Row {data-width=400}
-------------------------------------
### active cases {.value-box}

```{r plot active cases box}
valueBox(
  value = paste(format(total_active, big.mark = ","), "", sep = " "),
  caption = "ACTIVE CASES",
  icon = "fas fa-user-md",
  color = mustard
)
```

### recovered {.value-box}

```{r plot recovered box}
valueBox(
  value = paste(format(total_recovered, big.mark = ","), " (", round(percentage_recovered, 1),"%)", sep = ""),
  caption = "RECOVERED",
  icon = "fas fa-user-md",
  color = agri_green
)
```

Row {data-width=400}
-------------------------------------
### **Daily tests and positive cases**
```{r plot new tests and cases bubble graph}
ggplot(bubble_data, aes(x=date, y=new_tests, size = new_cases)) +
    geom_point(alpha=0.5) +
    scale_size(range = c(.1, 10)) +
    labs(caption = "Updated: June 2021",
     x = NULL,
     y = NULL) +
    scale_x_date(date_breaks = "1 month", date_labels = "%d/%m") +
    theme_minimal() + theme(legend.position="none")
```




### **Confirmed infections (last 20 days)**
```{r plot confirmed cases - last 20 days}
ggplot(confirmed, aes(rev(date), new_cases)) + 
  geom_col(position="dodge") +
  geom_text(aes(label = new_cases), position = position_dodge(0.9), hjust=-0.1) + # add labels
  labs(caption = "Updated: June 2021",
       x = NULL,
       y = NULL) +
  scale_x_date(date_breaks = "1 day", date_labels = "%m-%d-%Y") +
  scale_y_continuous(labels = NULL, breaks = NULL)  +# remove amount ticks
  coord_flip() +
  theme_minimal()
```

# Project Writeup
Explain overall code here

# Background Research

* Excess deaths
* Doubling rate

# Sources
* TODO